Science - USA (2020-09-04)

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By Wendy K. Tam Cho1,2,3,4,5 and Bruce E. Cain6,7

R

edistricting—the constitutionally man-

dated, decennial redrawing of elec-

toral district boundaries—can distort

representative democracy. An adept

map drawer can elicit a wide range of

election outcomes just by regrouping

voters (see the figure). When there are thou-

sands of precincts, the number of possible

partitions is astronomical, giving rise to enor-

mous potential manipulation. Recent techno-

logical advances have enabled new computa-

tional redistricting algorithms, deployable

on supercomputers, that can explore tril-

lions of possible electoral maps without hu-

man intervention. This leaves us to wonder

if Supreme Court Justice Elena Kagan was

prescient when she lamented, “(t)he 2010 re-

districting cycle produced some of the worst

partisan gerrymanders on record. The tech-

nology will only get better, so the 2020 cycle

will only get worse’’ (Gill v. Whitford). Given

the irresistible urge of biased politicians to

use computers to draw gerrymanders and

the capability of computers to autonomously

produce maps, perhaps we should just

let the machines take over. The North

Carolina Senate recently moved in this

direction when it used a state lottery

machine to choose from among 1000

computer-drawn maps. However, im-

proving the process and, more impor-

tantly, the outcomes results not from

developing technology but from our

ability to understand its potential and

to manage its (mis)use.

It has taken many years to develop

the computing hardware, derive the

theoretical basis, and implement

the algorithms that automate map

creation (both generating enormous

numbers of maps and uniformly sam-

pling them ) ( 1 – 4 ). Yet these innova-

tions have been “easy” compared with

the very difficult problem of ensur-

ing fair political representation for

a richly diverse society. Redistricting

is a complex sociopolitical issue for

which the role of science and the

advances in computing are nonobvi-

ous. Accordingly, we must not allow a

fascination with technological meth-

ods to obscure a fundamental truth:

The most important decisions in devising an

electoral map are grounded in philosophi-

cal or political judgments about which the

technology is irrelevant. It is nonsensical to

completely transform a debate over philo-

sophical values into a mathematical exercise.

As technology advances, computers are

able to digest progressively larger quanti-

ties of data per time unit. Yet more com-

putation is not equivalent to more fairness.

More computation fuels an increased ca-

pacity for identifying patterns within data.

But more computation has no relationship

with the moral and ethical standards of an

evolving and developing society. Neither

computation nor even an equitable process

guarantees a fair outcome.

The way forward is for people to work

collaboratively with machines to produce

results not otherwise possible. To do this,

we must capitalize on the strengths and

minimize the weaknesses of both artificial

intelligence (AI) and human intelligence.

Ensuring representational fairness requires

metacognition that integrates creative and

benevolent compromises. Humans have the

advantage over machines in metacognition.

Machines have the advantage in produc-

ing large numbers of rote computations.

Although machines produce information,

humans must infuse values to make judg-

ments about how this information should

be used ( 5 ).

Accordingly, machines can be tasked

with the menial aspects of cognition—the

meticulous exploration of the astronomi-

cal number of ways in which a state can be

partitioned. This helps us classify and un-

derstand the range of possibilities and the

interplay of competing interests. Machines

enhance and inform intelligent decision-

making by helping us navigate the unfath-

omably large and complex informational

landscape. Left to their own devices, hu-

mans have shown themselves to be unable

to resist the temptation to chart biased

paths through that terrain.

HOW MIGHT COLLABORATION HAPPEN?

The ideal redistricting process begins with

humans articulating the initial criteria for

the construction of a fair electoral map (e.g.,

population equality, compactness measures,

constraints on breaking political subdivi-

sions, and representation thresholds). Here,

the concerns of many different communities

of interest should be solicited and consid-

ered. Note that this starting point already re-

quires critical human interaction and consid-

erable deliberation. Determining what data

to use, and how, is not automatable (e.g., citi-

zen voting age versus voting age population,

relevant past elections, and how to

forecast future vote choices). Partisan

measures (e.g., mean-median dif-

ference, competitiveness, likely seat

outcome, and efficiency gap) as well

as vote prediction models, which are

often contentious in court, should be

transparently specified.

Once we have settled on the inputs

to the algorithm, the computational

analysis produces a large sample of

redistricting plans that satisfy these

principles. Trade-offs usually arise

(e.g., adhering to compactness rules

might require splitting jagged cit-

ies). Humans must make value-laden

judgments about these trade-offs, of-

ten through contentious debate.

The process would then iterate.

After some contemplation, we may

decide, perhaps, on two, not three,

majority-minority districts so that

a particular town is kept together.

These refined goals could then be

specified for another computational

analysis round with further delib-

eration to follow. Sometimes a Pareto

improvement principle applies, with

POLICY FORUM

Human-centered redistricting

automation in the age of AI

Human-machine collaboration and transparency are key

12 voters (6 R, 6 D) 4 districts: 1 R, 3 D

4 districts: 3 R, 1 D 4 districts: 2 R, 2 D

R

R

R

R R

D

D

D

D

D

D

R

Republican (R) district Democrat (D) district

R

R

R

R R

D

D

D

D

D

R

D

R

R

R

R R

D

D

D

D

D

D

R

R

R

R

R R

D

D

D

D

D

D

R

Time to regroup

Markedly different outcomes can emerge when six Republicans and six

Democrats in these 12 geographic units are grouped into four districts.

A 50-50 party split can be turned into a 3:1 advantage for either party.

When redistricting a state with thousands of precincts, the potential for

political manipulation is enormous.

4 SEPTEMBER 2020 • VOL 369 ISSUE 6508 1179

Published by AAAS